A Novel Real-Time Reference Key Frame Scan Matching Method

Abstract

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems.

Real-Time Efficient Exploration in Unknown Dynamic Environments Using MAVs

“…It is obvious from the presented figures that the DT method achieves the objectives above. Part of the constructed map (Map I), using reference key frame algorithm [24], is presented as a binary image, as shown in Figure 5a with its DT depicted in Figure 5b. The walls are successfully extended, and a candidate safe area is prepared which is represented by brightest color, between the walls to be as far as possible from all current static obstacles, as shown in Figure 5b.…”

“…Figure 1 shows the overview structure of the proposed method. The reference key frame (RKF) algorithm is used to construct the map of the unknown environment and localize the vehicle inside this map [24]. A filtering approach is proposed by Mohamed et al [25] that can distinguish between static and dynamic objects in the exploring environment.…”

Improved Reference Key Frame Algorithm

“…BACKGROUND (Mohamed et al, 2017) proposed the reference key frame (RKF) algorithm. This algorithm depends on extracting lines from the laser scanner point cloud.…”

“…As mentioned before, the proposed RKF algorithm comprises two methods, namely RKF and ICP algorithms. According to the experiments results in (Mohamed et al, 2017), the RKF algorithm exhibits more robustness than the ICP algorithm. Therefore, each method has its corresponding covariance matrix of the measurement noise ( ).…”

“…On the other hand, reference key frame (RKF) algorithm proposed 2D real-time scan matching algorithm that depends on a sole sensor, laser scan rangefinder, and does not require external aided sensors (Mohamed et al, 2017). The RKF algorithm attempts to mitigate the accumulated errors that contaminate the local and global scan matching.…”